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Python, Data Science and Projects
About Python for Data Science

Python is the most popular programming language for Data Science as on Today. Python is powerful , easy to learn and flexible tool for coding Data Science and Machine Learning algorithms. In recent years, Python has evolved immensely with respect to Data Science sphere, with a huge community around Python creating quite a few power data science and analytics packages such as Pandas, Numpy, Scikit Learn, Scipy and more. As a result, analyzing data, modeling machine learning algorithms with Python has never been easier.

Python for Data Science - Course Objectives
  • Python Fundamentals : Understanding Python syntax, data types, operators, conditional statements, functions. Writing simple Python scripts for Data Science
  • Data Science essentials: Understand core concepts of Data Science. Data exploration, data mugging, data pre-processing and transforming Data for further analysis. Gain familiarity with Python packages Numpy and Pandas
  • Python for Machine Learning(ML): Understand core concepts of Machine Learning and practice simple ML algorithms using Python package Scikit Learn
Course Contents
Introduction To Data Science With Python
  • What is analytics & Data Science?
  • Common Terms in Analytics
  • Analytics vs. Data warehousing, OLAP, MIS Reporting
  • Relevance in industry and need of the hour
  • Types of problems and business objectives in various industries
  • Critical success drivers
  • Overview of analytics tools & their popularity
  • Analytics Methodology & problem solving framework
Python: Essential (Core)
  • Overview of Python- Starting with Python
  • Introduction to installation of Python
  • Introduction to Python Editors & IDE’s(Canopy, pycharm, Jupyter, Rodeo, Ipython etc…)
  • Understand Jupyter notebook & Customize Settings
  • Concept of Packages/Libraries – Important packages(NumPy, SciPy, scikit-learn, Pandas, Matplotlib, etc)
  • Installing & loading Packages & Name Spaces
  • Data Types & Data objects/structures (strings, Tuples, Lists, Dictionaries)
  • List and Dictionary Comprehensions
Scientific Distribution Used In Python For Data Science
  • Numpy, Spify, Pandas, Scikitlearn statmodels, Nltk etc
Accessing/Importing And Exporting Data Using Python Modules
  • Importing Data from various sources (Csv, txt, excel, access etc)
  • Database Input (Connecting to database)
  • Viewing Data objects – subsetting, methods
  • Exporting Data to various formats
  • Important python modules: Pandas, beautifulsoup
Data Manipulation - Cleansing -Munging Using Python Modules
  • Cleansing Data with Python
  • Data Manipulation steps(Sorting, filtering, duplicates, merging, appending, subsetting, derived variables, sampling, Data type conversions, renaming, formatting etc)
  • Data manipulation tools(Operators, Functions, Packages, control structures, Loops, arrays etc)
  • Python Built-in Functions (Text, numeric, date, utility functions)
  • Python User Defined Functions
  • Stripping out extraneous information
  • Normalizing data
  • Formatting data
  • Important Python modules for data manipulation (Pandas, Numpy, re, math, string, datetime etc)
Data Analysis - visualization Using Python
  • Introduction exploratory data analysis
  • Descriptive statistics, Frequency Tables and summarization
  • Univariate Analysis (Distribution of data & Graphical Analysis)
  • Bivariate Analysis(Cross Tabs, Distributions & Relationships, Graphical Analysis)
  • Creating Graphs- Bar/pie/line chart/histogram/ boxplot/ scatter/ density etc)
  • Important Packages for Exploratory Analysis(NumPy Arrays, Matplotlib, seaborn, Pandas and scipy.stats etc)
Introduction To Statistics
  • Basic Statistics – Measures of Central Tendencies and Variance
  • Building blocks – Probability Distributions – Normal distribution – Central Limit Theorem
  • Inferential Statistics -Sampling – Concept of Hypothesis Testing
  • Statistical Methods – Z/t-tests( One sample, independent, paired), Anova, Correlations and Chi-square
  • Important modules for statistical methods: Numpy, Scipy, Pandas
Introduction To Predictive Modeling
  • Concept of model in analytics and how it is used?
  • Common terminology used in analytics & modeling process
  • Popular modeling algorithms
  • Types of Business problems – Mapping of Techniques
  • Different Phases of Predictive Modeling